Cardiovascular Disease Detection Using Machine Learning
Journal
Computación y Sistemas
ISSN
2007-9737
1405-5546
Date Issued
2022
Author(s)
Ibarra, Rodrigo
León, Jaime
Ávila, Iván
Type
text::journal::journal article
Abstract
The detection of Cardiovascular Diseases (CVDs) prematurely is of great interest for the Healthcare Industry. According to the World Health Organization, heart diseases represent 32% of global deaths by 2019. In this work, we propose building an interpretable machine learning model to detect CVDs. For this, we use a public dataset consisting of over 320 thousand records and 279 features. We explore the performance of three well-known classifiers and we build them using hyper-parameter techniques. For interpretability, feature relevance is tested. After the experimental results, we found Random Forest to performed the best with 94% of accuracy and 81% of area under the ROC curve. We also implement an easy web application as a tool for detecting CVDs using relevant features information. © 2022 Instituto Politecnico Nacional. All rights reserved.